Reliability

Context rot degrades an AI agent's accuracy as a conversation grows, even with room left in the context window. What causes it, when it starts, and how to test for it.

AI agent observability tools get compared on tracing and eval features. Here is the checklist that matters once your agent has its own paying customers.

AI agent memory lets an agent recall facts across sessions, but a shared memory store leaks between tenants and fails quietly. What to store, drop, and test before it reaches a customer.

Prompt injection testing for AI agents has to check tool calls and tenant boundaries, not just chat replies. A concrete test method with real examples.

AI agent observability means tracing every tool call and model call, then turning those traces into numbers your own customers can see, tenant by tenant.

Deflection rate can look great and still hide a broken support product. The formula, real benchmarks, and the cost-per-resolution number that matters.